“What’s So Special about Special Collections?” Or, Assessing the Value Special Collections Bring to Academic Libraries
Notice bibliographique
Résumé
Objective – The objective of this study was to examine and call attention to the current deficiency in standardized performance measures and usage metrics suited to assessing the value and impact of special collections and archives and their contributions to the mission of academic research libraries and to suggest possible approaches to overcoming the deficiency. Methods – The authors reviewed attempts over the past dozen years by the Association of Research Libraries (ARL) and the Association of College and Research Libraries (ACRL) to highlight the unique types of value that special collections and archival resources contribute to academic research libraries. They also examined the results of a large survey of special collections and archives conducted by OCLC Research in 2010. In addition, they investigated efforts by the Society of American Archivists (SAA) dating back to the 1940s to define standardized metrics for gathering and comparing data about archival operations. Finding that the library and archival communities have thus far failed to develop and adopt common metrics and methods for gathering data about the activities of special collections and archives, the authors explored the potential benefits of borrowing concepts for developing user-centered value propositions and metrics from the business community. Results – This study found that there has been a lack of consensus and precision concerning the definition of “special collections” and the value propositions they offer, and that most attempts have been limited in their usefulness because they were collections-centric. The study likewise reaffirmed a lack of consensus regarding how to define and measure basic operations performed by special collections and archives, such as circulating materials to users in supervised reading rooms. The review of concepts and metrics for assessing value in the business community, however, suggested new approaches to defining metrics that may be more successful. Conclusion – The authors recommend shifting from collection-centric to user-centric approaches and identifying appropriately precise metrics that can be consistently and widely applied to facilitate cross-institutional comparisons. Adopting a user-centric perspective, they argue, will provide a broader picture of how scholars interact with special collections at different points in the research process, both inside and outside of supervised reading rooms, as well as how undergraduate students change their thinking about evidence through interaction with primary sources. They authors outline the potential benefits of substituting the commonly used “reader-day” metric for tabulating reading room visits with a “reader-hour” metric and correlating it with item usage data in order to gauge the intensity of reading room use. They also discuss the potential benefits of assessing impact of instructional outreach in special collections and archives through measures of student confidence in pursuing research projects that involve primary sources.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,033 | 0,164 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,011 | 0,022 |
| Études des sciences et des technologies | 0,005 | 0,015 |
| Communication savante | 0,026 | 0,034 |
| Science ouverte | 0,002 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».